DonorsChoose

DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.

Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:

• How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
• How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
• How to focus volunteer time on the applications that need the most assistance

The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.

About the DonorsChoose Data Set

The train.csv data set provided by DonorsChoose contains the following features:

Feature	Description
project_id	A unique identifier for the proposed project. Example: p036502
project_title	Title of the project. Examples: Art Will Make You Happy! First Grade Fun
project_grade_category	Grade level of students for which the project is targeted. One of the following enumerated values: Grades PreK-2 Grades 3-5 Grades 6-8 Grades 9-12
project_subject_categories	One or more (comma-separated) subject categories for the project from the following enumerated list of values: Applied Learning Care & Hunger Health & Sports History & Civics Literacy & Language Math & Science Music & The Arts Special Needs Warmth Examples: Music & The Arts Literacy & Language, Math & Science
school_state	State where school is located (Two-letter U.S. postal code). Example: WY
project_subject_subcategories	One or more (comma-separated) subject subcategories for the project. Examples: Literacy Literature & Writing, Social Sciences
project_resource_summary	An explanation of the resources needed for the project. Example: My students need hands on literacy materials to manage sensory needs!
project_essay_1	First application essay*
project_essay_2	Second application essay*
project_essay_3	Third application essay*
project_essay_4	Fourth application essay*
project_submitted_datetime	Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245
teacher_id	A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56
teacher_prefix	Teacher's title. One of the following enumerated values: nan Dr. Mr. Mrs. Ms. Teacher.
teacher_number_of_previously_posted_projects	Number of project applications previously submitted by the same teacher. Example: 2

^* See the section Notes on the Essay Data for more details about these features.

Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:

Feature	Description
id	A project_id value from the train.csv file. Example: p036502
description	Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25
quantity	Quantity of the resource required. Example: 3
price	Price of the resource required. Example: 9.95

Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:

The data set contains the following label (the value you will attempt to predict):

Label	Description
project_is_approved	A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved.

Notes on the Essay Data

Prior to May 17, 2016, the prompts for the essays were as follows:
• __project_essay_1:__ "Introduce us to your classroom"
• __project_essay_2:__ "Tell us more about your students"
• __project_essay_3:__ "Describe how your students will use the materials you're requesting"
• __project_essay_3:__ "Close by sharing why your project will make a difference"

Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:

• __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
• __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"

For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.

%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer

import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle

from tqdm import tqdm
import os

#from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter

1.1 Reading Data

project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')

print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)

Number of data points in train data (109248, 17)
--------------------------------------------------
The attributes of data : ['Unnamed: 0' 'id' 'teacher_id' 'teacher_prefix' 'school_state'
 'project_submitted_datetime' 'project_grade_category'
 'project_subject_categories' 'project_subject_subcategories'
 'project_title' 'project_essay_1' 'project_essay_2' 'project_essay_3'
 'project_essay_4' 'project_resource_summary'
 'teacher_number_of_previously_posted_projects' 'project_is_approved']

print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)

Number of data points in train data (1541272, 4)
['id' 'description' 'quantity' 'price']

	id	description	quantity	price
0	p233245	LC652 - Lakeshore Double-Space Mobile Drying Rack	1	149.00
1	p069063	Bouncy Bands for Desks (Blue support pipes)	3	14.95

1.2 preprocessing of project_subject_categories

catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_') # we are replacing the & value into 
    cat_list.append(temp.strip())
    
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)

from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
    my_counter.update(word.split())

cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))

1.3 preprocessing of project_subject_subcategories

sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039

# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python

sub_cat_list = []
for i in sub_catogories:
    temp = ""
    # consider we have text like this "Math & Science, Warmth, Care & Hunger"
    for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
        if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
            j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
        j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
        temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
        temp = temp.replace('&','_')
    sub_cat_list.append(temp.strip())

project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)

# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
    my_counter.update(word.split())
    
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))

Preprocessing of 'Teacher_prefix'

teacher_pre = []
for prefix in project_data['teacher_prefix'].values:
    if prefix==prefix:
        prefix = re.sub('[^A-Za-z0-9]','',prefix).lower()
        teacher_pre.append(prefix)
    else:
        teacher_pre.append(prefix)
    
project_data['teacher_prefix'] = teacher_pre

Preprocessing of project_grade_category

project_grade_cat = []
for grade in project_data['project_grade_category'].values:
    grade = grade.replace('-','_').lower()
    grade = grade.replace(' ','_').lower()
    project_grade_cat.append(grade)
project_data['project_grade_category'] = project_grade_cat

1.3 Text preprocessing

# merge two column text dataframe: 
project_data["essay"] = project_data["project_essay_1"].map(str) +\
                        project_data["project_essay_2"].map(str) + \
                        project_data["project_essay_3"].map(str) + \
                        project_data["project_essay_4"].map(str)

project_data['essay'] = list(project_data["essay"].apply(lambda x: x.lower()))

project_data.head(2)

	Unnamed: 0	id	teacher_id	teacher_prefix	school_state	project_submitted_datetime	project_grade_category	project_title	project_essay_1	project_essay_2	project_essay_3	project_essay_4	project_resource_summary	teacher_number_of_previously_posted_projects	project_is_approved	clean_categories	clean_subcategories	essay
0	160221	p253737	c90749f5d961ff158d4b4d1e7dc665fc	Mrs.	IN	2016-12-05 13:43:57	Grades PreK-2	Educational Support for English Learners at Home	My students are English learners that are work...	\"The limits of your language are the limits o...	NaN	NaN	My students need opportunities to practice beg...	0	0	Literacy_Language	ESL Literacy	My students are English learners that are work...
1	140945	p258326	897464ce9ddc600bced1151f324dd63a	Mr.	FL	2016-10-25 09:22:10	Grades 6-8	Wanted: Projector for Hungry Learners	Our students arrive to our school eager to lea...	The projector we need for our school is very c...	NaN	NaN	My students need a projector to help with view...	7	1	History_Civics Health_Sports	Civics_Government TeamSports	Our students arrive to our school eager to lea...

#### 1.4.2.3 Using Pretrained Models: TFIDF weighted W2V

# printing some random reviews
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
print(project_data['essay'].values[99999])
print("="*50)

My students are English learners that are working on English as their second or third languages. We are a melting pot of refugees, immigrants, and native-born Americans bringing the gift of language to our school. \r\n\r\n We have over 24 languages represented in our English Learner program with students at every level of mastery.  We also have over 40 countries represented with the families within our school.  Each student brings a wealth of knowledge and experiences to us that open our eyes to new cultures, beliefs, and respect.\"The limits of your language are the limits of your world.\"-Ludwig Wittgenstein  Our English learner's have a strong support system at home that begs for more resources.  Many times our parents are learning to read and speak English along side of their children.  Sometimes this creates barriers for parents to be able to help their child learn phonetics, letter recognition, and other reading skills.\r\n\r\nBy providing these dvd's and players, students are able to continue their mastery of the English language even if no one at home is able to assist.  All families with students within the Level 1 proficiency status, will be a offered to be a part of this program.  These educational videos will be specially chosen by the English Learner Teacher and will be sent home regularly to watch.  The videos are to help the child develop early reading skills.\r\n\r\nParents that do not have access to a dvd player will have the opportunity to check out a dvd player to use for the year.  The plan is to use these videos and educational dvd's for the years to come for other EL students.\r\nnannan
==================================================
The 51 fifth grade students that will cycle through my classroom this year all love learning, at least most of the time. At our school, 97.3% of the students receive free or reduced price lunch. Of the 560 students, 97.3% are minority students. \r\nThe school has a vibrant community that loves to get together and celebrate. Around Halloween there is a whole school parade to show off the beautiful costumes that students wear. On Cinco de Mayo we put on a big festival with crafts made by the students, dances, and games. At the end of the year the school hosts a carnival to celebrate the hard work put in during the school year, with a dunk tank being the most popular activity.My students will use these five brightly colored Hokki stools in place of regular, stationary, 4-legged chairs. As I will only have a total of ten in the classroom and not enough for each student to have an individual one, they will be used in a variety of ways. During independent reading time they will be used as special chairs students will each use on occasion. I will utilize them in place of chairs at my small group tables during math and reading times. The rest of the day they will be used by the students who need the highest amount of movement in their life in order to stay focused on school.\r\n\r\nWhenever asked what the classroom is missing, my students always say more Hokki Stools. They can't get their fill of the 5 stools we already have. When the students are sitting in group with me on the Hokki Stools, they are always moving, but at the same time doing their work. Anytime the students get to pick where they can sit, the Hokki Stools are the first to be taken. There are always students who head over to the kidney table to get one of the stools who are disappointed as there are not enough of them. \r\n\r\nWe ask a lot of students to sit for 7 hours a day. The Hokki stools will be a compromise that allow my students to do desk work and move at the same time. These stools will help students to meet their 60 minutes a day of movement by allowing them to activate their core muscles for balance while they sit. For many of my students, these chairs will take away the barrier that exists in schools for a child who can't sit still.nannan
==================================================
How do you remember your days of school? Was it in a sterile environment with plain walls, rows of desks, and a teacher in front of the room? A typical day in our room is nothing like that. I work hard to create a warm inviting themed room for my students look forward to coming to each day.\r\n\r\nMy class is made up of 28 wonderfully unique boys and girls of mixed races in Arkansas.\r\nThey attend a Title I school, which means there is a high enough percentage of free and reduced-price lunch to qualify. Our school is an \"open classroom\" concept, which is very unique as there are no walls separating the classrooms. These 9 and 10 year-old students are very eager learners; they are like sponges, absorbing all the information and experiences and keep on wanting more.With these resources such as the comfy red throw pillows and the whimsical nautical hanging decor and the blue fish nets, I will be able to help create the mood in our classroom setting to be one of a themed nautical environment. Creating a classroom environment is very important in the success in each and every child's education. The nautical photo props will be used with each child as they step foot into our classroom for the first time on Meet the Teacher evening. I'll take pictures of each child with them, have them developed, and then hung in our classroom ready for their first day of 4th grade.  This kind gesture will set the tone before even the first day of school! The nautical thank you cards will be used throughout the year by the students as they create thank you cards to their team groups.\r\n\r\nYour generous donations will help me to help make our classroom a fun, inviting, learning environment from day one.\r\n\r\nIt costs lost of money out of my own pocket on resources to get our classroom ready. Please consider helping with this project to make our new school year a very successful one. Thank you!nannan
==================================================
My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations. \r\n\r\nThe materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills. \r\nThey also want to learn through games, my kids don't want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
==================================================
The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires. -William A. Ward\r\n\r\nMy school has 803 students which is makeup is 97.6% African-American, making up the largest segment of the student body. A typical school in Dallas is made up of 23.2% African-American students. Most of the students are on free or reduced lunch. We aren't receiving doctors, lawyers, or engineers children from rich backgrounds or neighborhoods. As an educator I am inspiring minds of young children and we focus not only on academics but one smart, effective, efficient, and disciplined students with good character.In our classroom we can utilize the Bluetooth for swift transitions during class. I use a speaker which doesn't amplify the sound enough to receive the message. Due to the volume of my speaker my students can't hear videos or books clearly and it isn't making the lessons as meaningful. But with the bluetooth speaker my students will be able to hear and I can stop, pause and replay it at any time.\r\nThe cart will allow me to have more room for storage of things that are needed for the day and has an extra part to it I can use.  The table top chart has all of the letter, words and pictures for students to learn about different letters and it is more accessible.nannan
==================================================

# https://stackoverflow.com/a/47091490/4084039
import re

def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)

    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase

sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)

My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations. \r\n\r\nThe materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills. \r\nThey also want to learn through games, my kids do not want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan
==================================================

# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)

My kindergarten students have varied disabilities ranging from speech and language delays, cognitive delays, gross/fine motor delays, to autism. They are eager beavers and always strive to work their hardest working past their limitations.     The materials we have are the ones I seek out for my students. I teach in a Title I school where most of the students receive free or reduced price lunch.  Despite their disabilities and limitations, my students love coming to school and come eager to learn and explore.Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting? This is how my kids feel all the time. The want to be able to move as they learn or so they say.Wobble chairs are the answer and I love then because they develop their core, which enhances gross motor and in Turn fine motor skills.   They also want to learn through games, my kids do not want to sit and do worksheets. They want to learn to count by jumping and playing. Physical engagement is the key to our success. The number toss and color and shape mats can make that happen. My students will forget they are doing work and just have the fun a 6 year old deserves.nannan

#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)

My kindergarten students have varied disabilities ranging from speech and language delays cognitive delays gross fine motor delays to autism They are eager beavers and always strive to work their hardest working past their limitations The materials we have are the ones I seek out for my students I teach in a Title I school where most of the students receive free or reduced price lunch Despite their disabilities and limitations my students love coming to school and come eager to learn and explore Have you ever felt like you had ants in your pants and you needed to groove and move as you were in a meeting This is how my kids feel all the time The want to be able to move as they learn or so they say Wobble chairs are the answer and I love then because they develop their core which enhances gross motor and in Turn fine motor skills They also want to learn through games my kids do not want to sit and do worksheets They want to learn to count by jumping and playing Physical engagement is the key to our success The number toss and color and shape mats can make that happen My students will forget they are doing work and just have the fun a 6 year old deserves nannan

# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
            "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
            'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
            'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
            'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
            's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
            've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
            "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
            "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
            'won', "won't", 'wouldn', "wouldn't"]

Stratified Distribution between Train-Test-Cv(64-20-16)

from sklearn.model_selection import train_test_split as tts
X_train,X_test,y_train,y_test = tts(project_data,project_data['project_is_approved'],test_size = 0.2, stratify = project_data['project_is_approved'])
X_train.drop(['project_is_approved'],axis=1,inplace=True)
X_test.drop(['project_is_approved'],axis=1,inplace=True)
#X_cv.drop(['project_is_approved'],axis=1,inplace=True)
print(X_train.shape)
print(X_test.shape)

X_train = pd.read_csv('X_train')
X_test = pd.read_csv('X_test')
y_train = pd.read_csv('Y_train',names = ['Unnamed:0','project_is_approved'])
y_test = pd.read_csv('Y_test',names = ['Unnamed:0','project_is_approved'])

project_grade_cat_train = []
for grade in X_train['project_grade_category'].values:
    grade = grade.replace('-','_').lower()
    grade = grade.replace(' ','_').lower()
    project_grade_cat_train.append(grade)
X_train['project_grade_category'] = project_grade_cat_train

project_grade_cat_test = []
for grade in X_test['project_grade_category'].values:
    grade = grade.replace('-','_').lower()
    grade = grade.replace(' ','_').lower()
    project_grade_cat_test.append(grade)
X_test['project_grade_category'] = project_grade_cat_test

1.4 Preprocessing of Essay on Trainig data

# Combining all the above stundents 
from tqdm import tqdm
preprocessed_essays_train = []
# tqdm is for printing the status bar
for sentance in tqdm(X_train['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
    preprocessed_essays_train.append(sent.lower().strip())

100%|██████████████████████████████████████████████████████████████████████████| 87398/87398 [00:48<00:00, 1798.77it/s]

1.4 Preprocessing of Essay on Test data

preprocessed_essays_test = []
# tqdm is for printing the status bar
for sentance in tqdm(X_test['essay'].values):
    sent = decontracted(sentance)
    sent = sent.replace('\\r', ' ')
    sent = sent.replace('\\"', ' ')
    sent = sent.replace('\\n', ' ')
    sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
    # https://gist.github.com/sebleier/554280
    sent = ' '.join(e for e in sent.split() if e.lower() not in stopwords)
    preprocessed_essays_test.append(sent.lower().strip())

100%|██████████████████████████████████████████████████████████████████████████| 21850/21850 [00:12<00:00, 1731.19it/s]

# after preprocesing
preprocessed_essays[20000]

'my kindergarten students varied disabilities ranging speech language delays cognitive delays gross fine motor delays autism they eager beavers always strive work hardest working past limitations the materials ones i seek students i teach title i school students receive free reduced price lunch despite disabilities limitations students love coming school come eager learn explore have ever felt like ants pants needed groove move meeting this kids feel time the want able move learn say wobble chairs answer i love develop core enhances gross motor turn fine motor skills they also want learn games kids not want sit worksheets they want learn count jumping playing physical engagement key success the number toss color shape mats make happen my students forget work fun 6 year old deserves nannan'

1.4 Preprocessing of `project_title`

1.4 Preprocessing of Title on Training data

# similarly you can preprocess the titles also
preprocessed_titles_train =[]
for title in tqdm(X_train['project_title'].values):
        des = decontracted(title)
        des = des.replace("\\r",' ')
        des = des.replace('\\"',' ')
        des = des.replace('\\n',' ')
        des = re.sub('[^A-Za-z0-9]+',' ',des)
        des = ' '.join(e for e in des.split() if e.lower() not in stopwords)
        preprocessed_titles_train.append(des.lower().strip())        

100%|█████████████████████████████████████████████████████████████████████████| 87398/87398 [00:02<00:00, 37267.12it/s]

1.4 Preprocessing of Title on Test data

preprocessed_titles_test =[]
for title in tqdm(X_test['project_title'].values):
        des = decontracted(title)
        des = des.replace("\\r",' ')
        des = des.replace('\\"',' ')
        des = des.replace('\\n',' ')
        des = re.sub('[^A-Za-z0-9]+',' ',des)
        des = ' '.join(e for e in des.split() if e.lower() not in stopwords)
        preprocessed_titles_test.append(des.lower().strip()) 

100%|█████████████████████████████████████████████████████████████████████████| 21850/21850 [00:00<00:00, 36868.93it/s]

Sentimental Analysis of Essay

import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
senti = SentimentIntensityAnalyzer()
positive_tr,positive_ts,positive_cv=[],[],[]
negative_tr ,negative_ts,negative_cv= [],[],[]
neutral_tr,neutral_ts,neutral_cv = [],[],[]
comp_tr ,comp_ts,comp_cv= [],[],[]

    

Training Data Sentiment

for i in tqdm(X_train['essay']):
    positive_tr.append(senti.polarity_scores(i)['pos'])
    negative_tr.append(senti.polarity_scores(i)['neg'])
    neutral_tr.append(senti.polarity_scores(i)['neu'])
    comp_tr.append(senti.polarity_scores(i)['compound'])
X_train['pos'] = positive_tr
X_train['neg'] = negative_tr
X_train['neu'] = neutral_tr
X_train['comp'] = comp_tr

Test Data Sentiment

for i in tqdm(X_test['essay']):
    positive_ts.append(senti.polarity_scores(i)['pos'])
    negative_ts.append(senti.polarity_scores(i)['neg'])
    neutral_ts.append(senti.polarity_scores(i)['neu'])
    comp_ts.append(senti.polarity_scores(i)['compound'])
X_test['pos'] = positive_ts
X_test['neg'] = negative_ts
X_test['neu'] = neutral_ts
X_test['comp'] = comp_ts

1.5 Preparing data for models

project_data.columns

Index(['Unnamed: 0', 'id', 'teacher_id', 'teacher_prefix', 'school_state',
       'project_submitted_datetime', 'project_grade_category', 'project_title',
       'project_essay_1', 'project_essay_2', 'project_essay_3',
       'project_essay_4', 'project_resource_summary',
       'teacher_number_of_previously_posted_projects', 'project_is_approved',
       'clean_categories', 'clean_subcategories', 'essay'],
      dtype='object')

we are going to consider

   - school_state : categorical data
   - clean_categories : categorical data
   - clean_subcategories : categorical data
   - project_grade_category : categorical data
   - teacher_prefix : categorical data

   - project_title : text data
   - text : text data
   - project_resource_summary: text data (optinal)

   - quantity : numerical (optinal)
   - teacher_number_of_previously_posted_projects : numerical
   - price : numerical

1.5.1 Vectorizing Categorical data

• https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/

One hot encoding of categories column in train,test,and cv data

# we use count vectorizer to convert the values into one 
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer( lowercase=False, binary=True)
vectorizer.fit(X_train['clean_categories'].values)
categories_one_hot_train = vectorizer.transform(preprocessed_essays_train)
categories_one_hot_test = vectorizer.transform(preprocessed_essays_test)
#categories_one_hot_cv = vectorizer.transform(preprocessed_essays_cv)
print(vectorizer.get_feature_names())
print("Shape of Train matrix after one hot encodig ",categories_one_hot_train.shape)
print("Shape of Test matrix after one hot encodig ",categories_one_hot_test.shape)
#print("Shape of CV matrix after one hot encodig ",categories_one_hot_cv.shape)

['AppliedLearning', 'Care_Hunger', 'Health_Sports', 'History_Civics', 'Literacy_Language', 'Math_Science', 'Music_Arts', 'SpecialNeeds', 'Warmth']
Shape of Train matrix after one hot encodig  (87398, 9)
Shape of Test matrix after one hot encodig  (21850, 9)

One hot encoding of sub categories column in train,test,and cv data

# we use count vectorizer_sub to convert the values into one 
from sklearn.feature_extraction.text import CountVectorizer
vectorizer_sub = CountVectorizer( lowercase=False, binary=True)
vectorizer_sub.fit(X_train['clean_subcategories'].values)
sub_categories_one_hot_train = vectorizer_sub.transform(preprocessed_titles_train)
sub_categories_one_hot_test = vectorizer_sub.transform(preprocessed_titles_test)
#sub_categories_one_hot_cv = vectorizer_sub.transform(preprocessed_titles_cv)
print(vectorizer_sub.get_feature_names())
print("Shape of Train matrix after one hot encodig ",sub_categories_one_hot_train.shape)
print("Shape of Test matrix after one hot encodig ",sub_categories_one_hot_test.shape)
#print("Shape of CV matrix after one hot encodig ",sub_categories_one_hot_cv.shape)

['AppliedSciences', 'Care_Hunger', 'CharacterEducation', 'Civics_Government', 'College_CareerPrep', 'CommunityService', 'ESL', 'EarlyDevelopment', 'Economics', 'EnvironmentalScience', 'Extracurricular', 'FinancialLiteracy', 'ForeignLanguages', 'Gym_Fitness', 'Health_LifeScience', 'Health_Wellness', 'History_Geography', 'Literacy', 'Literature_Writing', 'Mathematics', 'Music', 'NutritionEducation', 'Other', 'ParentInvolvement', 'PerformingArts', 'SocialSciences', 'SpecialNeeds', 'TeamSports', 'VisualArts', 'Warmth']
Shape of Train matrix after one hot encodig  (87398, 30)
Shape of Test matrix after one hot encodig  (21850, 30)

One hot encoding of teacher prefix column in train,test,and cv data

#https://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
#https://stackoverflow.com/questions/39303912/tfidfvectorizer-in-scikit-learn-valueerror-np-nan-is-an-invalid-document
vectorizer_prefix = CountVectorizer(lowercase = False,binary = True)
vectorizer_prefix = vectorizer_prefix.fit(X_train['teacher_prefix'].values.astype('U'))
prefix_one_hot_train = vectorizer_prefix.transform(X_train['teacher_prefix'].values.astype('U'))
#prefix_one_hot_cv = vectorizer.transform(X_cv['teacher_prefix'].values.astype('U'))
prefix_one_hot_test = vectorizer_prefix.transform(X_test['teacher_prefix'].values.astype('U'))
print(vectorizer_prefix.get_feature_names())
print("Shape of matrix after one hot encoding ", prefix_one_hot_train.shape)
#print("Shape of matrix after one hot encoding ", prefix_one_hot_cv.shape)
print("Shape of matrix after one hot encoding ", prefix_one_hot_test.shape)

['dr', 'mr', 'mrs', 'ms', 'nan', 'teacher']
Shape of matrix after one hot encoding  (87398, 6)
Shape of matrix after one hot encoding  (21850, 6)

One hot encoding of project grade column in train,test,and cv data

vectorizer_grade = CountVectorizer(lowercase = False,binary = True)
vectorizer_grade = vectorizer_grade.fit(X_train['project_grade_category'].values.astype('U'))
project_grade_one_hot_train = vectorizer_grade.transform(X_train['project_grade_category'].values.astype('U'))
#project_grade_one_hot_cv = vectorizer.transform(X_cv['project_grade_category'].values.astype('U'))
project_grade_one_hot_test = vectorizer_grade.transform(X_test['project_grade_category'].values.astype('U'))
print(vectorizer_grade.get_feature_names())
print("Shape of matrix after one hot encoding ", project_grade_one_hot_train.shape)
#print("Shape of matrix after one hot encoding ", project_grade_one_hot_cv.shape)
print("Shape of matrix after one hot encoding ", project_grade_one_hot_test.shape)

['grades_3_5', 'grades_6_8', 'grades_9_12', 'grades_prek_2']
Shape of matrix after one hot encoding  (87398, 4)
Shape of matrix after one hot encoding  (21850, 4)

One hot encoding of State column in train,test,and cv data

vectorizer_state = CountVectorizer(lowercase = False,binary = True)
vectorizer_state.fit(X_train['school_state'].values)
state_one_hot_train = vectorizer_state.transform(X_train['school_state'].values)
state_one_hot_test =  vectorizer_state.transform(X_test['school_state'].values)
#state_one_hot_cv = vectorizer.transform(X_cv['school_state'].values)
print(vectorizer_state.get_feature_names())
print("Shape of Train matrix after one hot encoding ", state_one_hot_train.shape)
print("Shape of Test matrix after one hot encoding ", state_one_hot_test.shape)
#print("Shape of cv matrix after one hot encoding ", state_one_hot_cv.shape)

['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY']
Shape of Train matrix after one hot encoding  (87398, 51)
Shape of Test matrix after one hot encoding  (21850, 51)

Essay and Title Words Count

Train Data

essay_word_counter_train = []
title_word_counter_train = []
for sent in preprocessed_essays_train:
    count = len(set(sent.split()))
    essay_word_counter_train.append(count)
for title in preprocessed_titles_train:
    count = len(set(title.split()))
    title_word_counter_train.append(count)
X_train['Essay_word_count'] = essay_word_counter_train
X_train['Title_word_count'] = title_word_counter_train

Test Data

essay_word_counter_test = []
title_word_counter_test = []
for sent in preprocessed_essays_test:
    count = len(set(sent.split()))
    essay_word_counter_test.append(count)
for title in preprocessed_titles_test:
    count = len(set(title.split()))
    title_word_counter_test.append(count)
X_test['Essay_word_count'] = essay_word_counter_test
X_test['Title_word_count'] = title_word_counter_test

1.5.2 Vectorizing Text data

1.5.2.1 Bag of words

# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
text_bow = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_bow.shape)

Shape of matrix after one hot encodig  (109248, 16623)

# you can vectorize the title also 
# before you vectorize the title make sure you preprocess it

TFIDF - Essays and Titles

Essay on Train-Test dataset

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer_essay_tfidf = TfidfVectorizer(min_df=10)
essay_tfidf_train = vectorizer_essay_tfidf.fit_transform(preprocessed_essays_train)
print("Shape of matrix after one hot encodig ",essay_tfidf_train.shape)

Shape of matrix after one hot encodig  (87398, 15130)

essay_tfidf_test = vectorizer_essay_tfidf.transform(preprocessed_essays_test)
print("Shape of matrix after one hot encoding ",essay_tfidf_test.shape)

Shape of matrix after one hot encoding  (21850, 15130)

Title on Train-Test dataset

vectorizer_title_tfidf = TfidfVectorizer(min_df = 10)
title_tfidf_train = vectorizer_title_tfidf.fit_transform(preprocessed_titles_train)
print("Shape of matrix after one hot encding ",title_tfidf_train.shape)

Shape of matrix after one hot encding  (87398, 2819)

title_tfidf_test = vectorizer_title_tfidf.transform(preprocessed_titles_test)
print("Shape of matrix after one hot encding ",title_tfidf_test.shape)

Shape of matrix after one hot encding  (21850, 2819)

1.5.2.3 Using Pretrained Models: Avg W2V

'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
    print ("Loading Glove Model")
    f = open(gloveFile,'r', encoding="utf8")
    model = {}
    for line in tqdm(f):
        splitLine = line.split()
        word = splitLine[0]
        embedding = np.array([float(val) for val in splitLine[1:]])
        model[word] = embedding
    print ("Done.",len(model)," words loaded!")
    return model
model = loadGloveModel('glove.42B.300d.txt')

# ============================
Output:
    
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495  words loaded!

# ============================

words = []
for i in preproced_texts:
    words.extend(i.split(' '))

for i in preproced_titles:
    words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))

inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
      len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")

words_courpus = {}
words_glove = set(model.keys())
for i in words:
    if i in words_glove:
        words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))


# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/

import pickle
with open('glove_vectors', 'wb') as f:
    pickle.dump(words_courpus, f)


'''

'\n# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039\ndef loadGloveModel(gloveFile):\n    print ("Loading Glove Model")\n    f = open(gloveFile,\'r\', encoding="utf8")\n    model = {}\n    for line in tqdm(f):\n        splitLine = line.split()\n        word = splitLine[0]\n        embedding = np.array([float(val) for val in splitLine[1:]])\n        model[word] = embedding\n    print ("Done.",len(model)," words loaded!")\n    return model\nmodel = loadGloveModel(\'glove.42B.300d.txt\')\n\n# ============================\nOutput:\n    \nLoading Glove Model\n1917495it [06:32, 4879.69it/s]\nDone. 1917495  words loaded!\n\n# ============================\n\nwords = []\nfor i in preproced_texts:\n    words.extend(i.split(\' \'))\n\nfor i in preproced_titles:\n    words.extend(i.split(\' \'))\nprint("all the words in the coupus", len(words))\nwords = set(words)\nprint("the unique words in the coupus", len(words))\n\ninter_words = set(model.keys()).intersection(words)\nprint("The number of words that are present in both glove vectors and our coupus",       len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")\n\nwords_courpus = {}\nwords_glove = set(model.keys())\nfor i in words:\n    if i in words_glove:\n        words_courpus[i] = model[i]\nprint("word 2 vec length", len(words_courpus))\n\n\n# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/\n\nimport pickle\nwith open(\'glove_vectors\', \'wb\') as f:\n    pickle.dump(words_courpus, f)\n\n\n'

# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
with open('glove_vectors', 'rb') as f:
    model = pickle.load(f)
    glove_words =  set(model.keys())

avg w2v vectors on Preprocessed Essays - Training data

# average Word2Vec
# compute average word2vec for each review.
avg_w2v_vectors_essays_train = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays_train): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_vectors_essays_train.append(vector)

print(len(avg_w2v_vectors_essays_train))
print(len(avg_w2v_vectors_essays_train[0]))

100%|██████████████████████████████████████████████████████████████████████████| 87398/87398 [00:29<00:00, 2997.41it/s]

87398
300

avg w2v vectors on Preprocessed Essays - Test data

avg_w2v_vectors_essays_test = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays_test): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if word in glove_words:
            vector += model[word]
            cnt_words += 1
    if cnt_words != 0:
        vector /= cnt_words
    avg_w2v_vectors_essays_test.append(vector)

print(len(avg_w2v_vectors_essays_test))
print(len(avg_w2v_vectors_essays_test[0]))

100%|██████████████████████████████████████████████████████████████████████████| 21850/21850 [00:07<00:00, 2895.02it/s]

21850
300

avg w2v vectors on Preprocessed Titles - Training data

#compute avg w2v for each title
avg_w2V_vectors_title_train =[]
for title in tqdm(preprocessed_titles_train):
    vector_title = np.zeros(300)
    cnt_words = 0
    for word in title.split():
        if word in glove_words:
            vector_title+=model[word]
            cnt_words+=1
    if cnt_words!=0:
        vector_title/=cnt_words
    avg_w2V_vectors_title_train.append(vector_title)
print(len(avg_w2V_vectors_title_train))
print(len(avg_w2V_vectors_title_train[0]))
        

100%|█████████████████████████████████████████████████████████████████████████| 87398/87398 [00:01<00:00, 59586.43it/s]

87398
300

avg w2v vectors on Preprocessed Titles - Test data

#compute avg w2v for each title
avg_w2V_vectors_title_test =[]
for title in tqdm(preprocessed_titles_test):
    vector_title = np.zeros(300)
    cnt_words = 0
    for word in title.split():
        if word in glove_words:
            vector_title+=model[word]
            cnt_words+=1
    if cnt_words!=0:
        vector_title/=cnt_words
    avg_w2V_vectors_title_test.append(vector_title)
print(len(avg_w2V_vectors_title_test))
print(len(avg_w2V_vectors_title_test[0]))
        

100%|█████████████████████████████████████████████████████████████████████████| 21850/21850 [00:00<00:00, 55110.88it/s]

21850
300

1.5.2.3 Using Pretrained Models: TFIDF weighted W2V

# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())

# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
    vector = np.zeros(300) # as word vectors are of zero length
    tf_idf_weight =0; # num of words with a valid vector in the sentence/review
    for word in sentence.split(): # for each word in a review/sentence
        if (word in glove_words) and (word in tfidf_words):
            vec = model[word] # getting the vector for each word
            # here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
            tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
            vector += (vec * tf_idf) # calculating tfidf weighted w2v
            tf_idf_weight += tf_idf
    if tf_idf_weight != 0:
        vector /= tf_idf_weight
    tfidf_w2v_vectors.append(vector)

print(len(tfidf_w2v_vectors))
print(len(tfidf_w2v_vectors[0]))

100%|█████████████████████████████████████████████████████████████████████████| 109248/109248 [07:32<00:00, 241.33it/s]

109248
300

# Similarly you can vectorize for title also

1.5.3 Vectorizing Numerical features

price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
X_train = pd.merge(X_train, price_data, on='id', how='left')
#X_cv = pd.merge(X_cv,price_data, on ='id',how = 'left')
X_test = pd.merge(X_test,price_data, on ='id',how = 'left')

PRICE

# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler,Normalizer

# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329.   ... 399.   287.73   5.5 ].
# Reshape your data either using array.reshape(-1, 1)

price_scalar = Normalizer()
price_scalar.fit(X_train['price'].values.reshape(1,-1)) # finding the mean and standard deviation of this data
#print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")

# Now standardize the data with above maen and variance.
price_standardized_train = price_scalar.transform(X_train['price'].values.reshape(1, -1)).reshape(-1,1)
#price_standardized_cv = price_scalar.transform(X_cv['price'][0:12000].values.reshape(-1,1))
price_standardized_test = price_scalar.transform(X_test['price'].values.reshape(1,-1)).reshape(-1,1)

Quantity

# standardized quantity columns 
quantity_scaler = Normalizer()
quantity_scaler.fit(X_train['quantity'].values.reshape(1,-1))
#print(f"Mean :{quantity_scaler.mean_[0]},Standard Deviation :{np.sqrt(quantity_scaler.var_[0])}")
quantity_standardized_train = quantity_scaler.transform(X_train['quantity'].values.reshape(1,-1)).reshape(-1,1)
#quantity_standardized_cv = quantity_scaler.transform(X_cv['quantity'][0:12000].values.reshape(-1,1))
quantity_standardized_test = quantity_scaler.transform(X_test['quantity'].values.reshape(1,-1)).reshape(-1,1)

No.of previously done Project

#standardized projects proposed by teachers
project_scaler = Normalizer()
project_scaler.fit(X_train['teacher_number_of_previously_posted_projects'].values.reshape(1,-1))
#print(f"Mean :{project_scaler.mean_[0]},Standard Deviation :{np.sqrt(project_scaler.var_[0])}")
project_standardized_train = project_scaler.transform(X_train['teacher_number_of_previously_posted_projects'].values.reshape(1,-1)).reshape(-1,1)
#project_standardized_cv = project_scaler.transform(X_cv['teacher_number_of_previously_posted_projects'][0:12000].values.reshape(-1,1))
project_standardized_test = project_scaler.transform(X_test['teacher_number_of_previously_posted_projects'].values.reshape(1,-1)).reshape(-1,1)

Essay Count

#standardized Essay Count
Essay_count_scaler = Normalizer()
Essay_count_scaler.fit(X_train['Essay_word_count'].values.reshape(1,-1))
#print(f"Mean :{Essay_count_scaler.mean_[0]},Standard Deviation :{np.sqrt(Essay_count_scaler.var_[0])}")
Essay_count_standardized_train = Essay_count_scaler.transform(X_train['Essay_word_count'].values.reshape(1,-1)).reshape(-1,1)
Essay_count_standardized_test = Essay_count_scaler.transform(X_test['Essay_word_count'].values.reshape(1,-1)).reshape(-1,1)
#Essay_count_standardized_cv = Essay_count_scaler.transform(X_cv['Essay_word_count'][:45000].values.reshape(-1,1))

Title Count

#standardized Title Count
title_count_scaler = Normalizer()
title_count_scaler.fit(X_train['Title_word_count'].values.reshape(1,-1))
#print(f"Mean :{title_count_scaler.mean_[0]},Standard Deviation :{np.sqrt(title_count_scaler.var_[0])}")
title_count_standardized_train = title_count_scaler.transform(X_train['Title_word_count'].values.reshape(1,-1)).reshape(-1,1)
title_count_standardized_test = title_count_scaler.transform(X_test['Title_word_count'].values.reshape(1,-1)).reshape(-1,1)
#title_count_standardized_cv = title_count_scaler.transform(X_cv['Title_word_count'][:45000].values.reshape(-1,1))

Essay positive Sentiment

# normalize positive sentiment of essay 
pos_senti_scaler = Normalizer()
pos_senti_scaler.fit(X_train['pos'].values.reshape(1,-1))
essay_pos_train = pos_senti_scaler.transform(X_train['pos'].values.reshape(1,-1)).reshape(-1,1)
essay_pos_test = pos_senti_scaler.transform(X_test['pos'].values.reshape(1,-1)).reshape(-1,1)

Essay Negative Sentiment

neg_senti_scaler = Normalizer()
neg_senti_scaler.fit(X_train['neg'].values.reshape(1,-1))
essay_neg_train = neg_senti_scaler.transform(X_train['neg'].values.reshape(1,-1)).reshape(-1,1)
essay_neg_test = neg_senti_scaler.transform(X_test['neg'].values.reshape(1,-1)).reshape(-1,1)

Essay Neutral Sentiment

neu_senti_scaler = Normalizer()
neu_senti_scaler.fit(X_train['neu'].values.reshape(1,-1))
essay_neu_train = neu_senti_scaler.transform(X_train['neu'].values.reshape(1,-1)).reshape(-1,1)
essay_neu_test = neu_senti_scaler.transform(X_test['neu'].values.reshape(1,-1)).reshape(-1,1)

Essay Compound Sentiment

comp_senti_scaler = Normalizer()
comp_senti_scaler.fit(X_train['comp'].values.reshape(1,-1))
essay_comp_train = comp_senti_scaler.transform(X_train['comp'].values.reshape(1,-1)).reshape(-1,1)
essay_comp_test = comp_senti_scaler.transform(X_test['comp'].values.reshape(1,-1)).reshape(-1,1)

Assignment 10: Clustering

• step 1: Choose any vectorizer (data matrix) that you have worked in any of the assignments, and got the best AUC value.
• step 2: Choose any of the feature selection/reduction algorithms ex: selectkbest features, pretrained word vectors, model based feature selection etc and reduce the number of features to 5k features
• step 3: Apply all three kmeans, Agglomerative clustering, DBSCAN
  • K-Means Clustering:
    ● Find the best ‘k’ using the elbow-knee method (plot k vs inertia_)
    
  • Agglomerative Clustering:
    ● Apply agglomerative algorithm and try a different number of clusters like 2,5 etc.
    ● You can take less data points (as this is very computationally expensive one) to perform hierarchical clustering because they do take a considerable amount of time to run.
    
  • DBSCAN Clustering:
    ● Find the best ‘eps’ using the elbow-knee method.
    ● You can take a smaller sample size for this as well.
• step 4: Summarize each cluster by manually observing few points from each cluster.
• step 5: You need to plot the word cloud with essay text for each cluster for each of algorithms mentioned in step 3.

2. Clustering

categorical, numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF)

# Please write all the code with proper documentation
#from xgboost import XGBClassifier
#import xgboost as xgb
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
from scipy import sparse
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X_tr = hstack((categories_one_hot_train[:45000],sub_categories_one_hot_train[:45000],prefix_one_hot_train[:45000],
project_grade_one_hot_train[:45000],state_one_hot_train[:45000],sparse.csr_matrix(price_standardized_train[:45000]),
        sparse.csr_matrix(quantity_standardized_train[:45000]),sparse.csr_matrix(project_standardized_train[:45000]),
               sparse.csr_matrix(Essay_count_standardized_train[:45000]),sparse.csr_matrix(title_count_standardized_train[:45000])
               ,sparse.csr_matrix(essay_pos_train[:45000]),sparse.csr_matrix(essay_neg_train[:45000]),sparse.csr_matrix(essay_neu_train[:45000]),
               sparse.csr_matrix(essay_comp_train[:45000]),essay_tfidf_train[:45000],title_tfidf_train[:45000])).tocsr()


X_ts =  hstack((categories_one_hot_test[:15000],sub_categories_one_hot_test[:15000],prefix_one_hot_test[:15000],
project_grade_one_hot_test[:15000],state_one_hot_test[:15000],sparse.csr_matrix(price_standardized_test[:15000]),
        sparse.csr_matrix(quantity_standardized_test[:15000]),sparse.csr_matrix(project_standardized_test[:15000]),
        sparse.csr_matrix(Essay_count_standardized_test[:15000]),sparse.csr_matrix(title_count_standardized_test[:15000])
                 ,sparse.csr_matrix(essay_pos_test[:15000]),sparse.csr_matrix(essay_neg_test[:15000]),sparse.csr_matrix(essay_neu_test[:15000]),
               sparse.csr_matrix(essay_comp_test[:15000]),essay_tfidf_test[:15000],title_tfidf_test[:15000])).tocsr()

tfidf_features_names = []
for i in vectorizer.get_feature_names():
    tfidf_features_names.append(i)
for i in vectorizer_sub.get_feature_names():
    tfidf_features_names.append(i)
for i in vectorizer_prefix.get_feature_names():
    tfidf_features_names.append(i)
for i in vectorizer_grade.get_feature_names():
    tfidf_features_names.append(i)
for i in vectorizer_state.get_feature_names():
    tfidf_features_names.append(i)

tfidf_features_names.append("Price")
tfidf_features_names.append("Quantity")
tfidf_features_names.append("Previously done projects")
tfidf_features_names.append("Essay Counts")
tfidf_features_names.append("Title Counts")
tfidf_features_names.append("Essay Pos Senti")
tfidf_features_names.append("Essay Neg Senti")
tfidf_features_names.append("Essay Neu Senti")
tfidf_features_names.append("Essay Comp Ssenti")

for i in vectorizer_essay_tfidf.get_feature_names():
    tfidf_features_names.append(i)
for i in vectorizer_title_tfidf.get_feature_names():
    tfidf_features_names.append(i)

from sklearn.feature_selection import SelectKBest,chi2,f_classif
X_new = SelectKBest(f_classif,k=5000).fit_transform(X_tr,y_train[:45000]["project_is_approved"])

X_new.shape

(45000, 5000)

2.5 Apply Kmeans

from sklearn.cluster import KMeans,AgglomerativeClustering,MiniBatchKMeans,DBSCAN

model_k_means = KMeans(n_clusters=2,init='k-means++',n_init=5,precompute_distances=True,max_iter=100)
model_k_means.fit(X_new)

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=100,
       n_clusters=2, n_init=5, n_jobs=None, precompute_distances=True,
       random_state=None, tol=0.0001, verbose=0)

k_means = [1,2,3,4,5,6,7,8,9,10]
euclidean_dist = []
n_iter_run = []
cluster_label = []
for k in tqdm(k_means):
    model = KMeans(n_clusters=k,init='k-means++',n_init=10,precompute_distances=True,max_iter=200,n_jobs=-1)
    model.fit(X_new)
    euclidean_dist.append(model.inertia_)
    n_iter_run.append(model.n_iter_)
    cluster_label.append(model.labels_)
    

100%|█████████████████████████████████████████████████████████████████████████████████| 10/10 [23:51<00:00, 191.30s/it]

K vs inertia_

fig = go.Figure()
fig.add_trace(go.Scatter(x = k_means,y = euclidean_dist,mode ='lines+markers+text',name = "Elbow Knee Curve",showlegend = True ))
fig.add_trace(go.Scatter(x = [2],y = [82002],mode = 'lines+markers',name = 'Elbow Point'))
fig.update_layout(title = 'K vs Euclidean Distance',
                  shapes=[
                # Line Vertical
            go.layout.Shape(
            type="line",
            x0=0,
            y0=82002,
            x1=2,
            y1=82002,
            line=dict(
                color="red",
                width=1
            ))],
                  xaxis = go.layout.XAxis(title = go.layout.xaxis.Title(text = 'K Hyperparameter for Clustering')),
                 yaxis = go.layout.YAxis(title = go.layout.yaxis.Title(text = "Euclidean Distance From centroid")))
fig.show()

As we can see from above plot that with increase in cluster the Euclidean distance from the centroid decreases .

We can see our elbow point at k=2 which is true as our class label is also 2.

Silhouette Score Variation For Each Cluster

from sklearn.metrics import silhouette_score
score = []
for j in tqdm(cluster_label[1:]):
    score.append(silhouette_score(X_new,j))

100%|███████████████████████████████████████████████████████████████████████████████████| 9/9 [27:53<00:00, 182.98s/it]

fig = go.Figure()
fig.add_trace(go.Scatter(x = k_means,y = score,mode ='lines+markers+text',name = "Score",showlegend = True ))
fig.update_layout(title = 'K vs Silhouette Score',
                  xaxis = go.layout.XAxis(title = go.layout.xaxis.Title(text = 'K Hyperparameter for Clustering')),
                 yaxis = go.layout.YAxis(title = go.layout.yaxis.Title(text = "Silhouette Score")))
fig.show()

Silhouette Score is ploted against k - hyperparameter and as we can see score for 2 is largest of all and that's what silhouette score explain :

As the value of score reaches toward 1 then cluster is well defined with no-overlapping.

As score is more closer to "zero" we can say cluster is overlapped i.e not well seperated .

If Score is in negative and reaching towards -1 we can say datapoints has wrongly clustered.

From above plot we can say that clustered hasn't been seperated but overlapped.

Essay representation Of each Cluster through WordCloud

model = KMeans(n_clusters=2,init='k-means++',n_init=10,precompute_distances=True,max_iter=300,n_jobs=-1)
model.fit(X_new)

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
       n_clusters=2, n_init=10, n_jobs=-1, precompute_distances=True,
       random_state=None, tol=0.0001, verbose=0)

X_new_0 = X_train.loc[list(np.where(model.labels_==0)[0])]
X_new_1 = X_train.loc[list(np.where(model.labels_==1)[0])]

from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
def WordCloud_visual(df,stopwords):
    Essay = list(df['essay'].values)
    tot_text = " "
    for text in Essay:
        tot_text = tot_text + text
    wordcloud = WordCloud(width = 800, height = 800, 
                background_color ='black', 
                stopwords = stopwords, 
                min_font_size = 10).generate(tot_text) 
  
    # plot the WordCloud image                        
    plt.figure(figsize = (8, 8), facecolor = None) 
    plt.imshow(wordcloud) 
    plt.axis("off") 
    plt.tight_layout(pad = 0) 

Cluster 1 WordCloud

WordCloud_visual(X_new_0,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_0['teacher_prefix'].value_counts().index ),
                        y =list(X_new_0['teacher_prefix'].value_counts()))])
fig.show()

X_new_0.groupby('teacher_prefix').agg({'Essay_word_count':{'mean_essay_count':'sum'},'price':{'mean_price':'sum'}
                                       })

	Essay_word_count	price
	mean_essay_count	mean_price
teacher_prefix
dr	655	1062.11
mr	461484	1645640.86
ms	1670239	4827162.77
teacher	99635	304451.91

plt.figure(figsize = (20,10))
X_new_0['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afc6fe2518>

X_new_0['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afca7770b8>

Cluster 2 WordCloud

WordCloud_visual(X_new_1,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_1['teacher_prefix'].value_counts().index ),
                        y =list(X_new_1['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_1['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afcc3d54e0>

X_new_1['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe8868f28>

Summary Looking Above two Cluster

The main point that we analyze looking EDA of above two clusters that cluster 2 has teacher prefix of only 1 category i.e 'mrs' and cluster 1 has teacher prefix of all other category except 'mrs ', so we can say cluster 2 is the whose teacher_prefix is 'mrs' and cluster 1 which don't have teacher prefix of 'mrs'.

The grade category and state column of each cluster is equally distributed

In cluster 1 essay column has 'classroom','learning','well','book' etc as frequently occured word and in cluster 2 essay column has 'My student', 'many student','student come','class' etc as frequently occured word.

Agglormerative Clustering with k = 2

X_new_agglo = X_new[:10000]
X_new_agglo = X_new_agglo.toarray()

model_agglo = AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='ward')
model_agglo.fit(X_new_agglo)

AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',
                        connectivity=None, distance_threshold=None,
                        linkage='ward', memory=None, n_clusters=2,
                        pooling_func='deprecated')

X_new_agglo_0 = X_train[:10000].loc[list(np.where(model_agglo.labels_==0)[0])]
X_new_agglo_1 = X_train[:10000].loc[list(np.where(model_agglo.labels_==1)[0])]

from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
def WordCloud_visual(df,stopwords):
    Essay = list(df['essay'].values)
    tot_text = " "
    for text in Essay:
        tot_text = tot_text + text
    wordcloud = WordCloud(width = 800, height = 800, 
                background_color ='black', 
                stopwords = stopwords, 
                min_font_size = 10).generate(tot_text) 
  
    # plot the WordCloud image                        
    plt.figure(figsize = (8, 8), facecolor = None) 
    plt.imshow(wordcloud) 
    plt.axis("off") 
    plt.tight_layout(pad = 0) 

WordCloud Representation of Essay

Cluster 1

WordCloud_visual(X_new_agglo_0,stopwords)

Cluster 2

WordCloud_visual(X_new_agglo_1,stopwords)

Agglormerative Clustering with k = 5

model_agglo = AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')
model_agglo.fit(X_new_agglo)

AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',
                        connectivity=None, distance_threshold=None,
                        linkage='ward', memory=None, n_clusters=5,
                        pooling_func='deprecated')

X_new_agglo_0 = X_train[:10000].loc[list(np.where(model_agglo.labels_==0)[0])]
X_new_agglo_1 = X_train[:10000].loc[list(np.where(model_agglo.labels_==1)[0])]
X_new_agglo_2 = X_train[:10000].loc[list(np.where(model_agglo.labels_==2)[0])]
X_new_agglo_3 = X_train[:10000].loc[list(np.where(model_agglo.labels_==3)[0])]
X_new_agglo_4 = X_train[:10000].loc[list(np.where(model_agglo.labels_==4)[0])]

Cluster 1

WordCloud_visual(X_new_agglo_0,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_agglo_0['teacher_prefix'].value_counts().index ),
                        y =list(X_new_agglo_0['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_agglo_0['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afcc701a90>

X_new_agglo_0['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe8e04908>

Cluster 2

WordCloud_visual(X_new_agglo_1,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_agglo_1['teacher_prefix'].value_counts().index ),
                        y =list(X_new_agglo_1['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_agglo_1['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe914aeb8>

X_new_agglo_0['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe99986a0>

Cluster 3

WordCloud_visual(X_new_agglo_2,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_agglo_2['teacher_prefix'].value_counts().index ),
                        y =list(X_new_agglo_2['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_agglo_2['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afcc7e9d68>

X_new_agglo_2['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe7c862b0>

Cluster 4

WordCloud_visual(X_new_agglo_3,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_agglo_3['teacher_prefix'].value_counts().index ),
                        y =list(X_new_agglo_3['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_agglo_3['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe78bc668>

X_new_agglo_3['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe7c86c18>

Cluster 5

WordCloud_visual(X_new_agglo_4,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_agglo_4['teacher_prefix'].value_counts().index ),
                        y =list(X_new_agglo_4['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_agglo_4['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe7b275c0>

X_new_agglo_4['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1afe7da4400>

Summary On the Basis of above EDA on each Cluster

We have some interesting result from above EDA :-

In cluster 1 , we have teacher_prefix of only one category 'ms'.

In cluster 2 , we have teacher_prefix of only one category 'mrs'.

In cluster 3 , we have teacher_prefix of two category 'mr' and ' teacher' .

In cluster 4 , we have teacher_prefix of only one category 'mrs'.

In cluster 5 , we have teacher_prefix of only one category 'mrs'.

By looking above it's seems that cluster 2,4,and 5 should belong to one cluster but there is difference in these cluster.

In cluster 4 , we have approximately all project_grade_category of one type ' grade_3_5'.

In cluster 5 , we have school_state of only one type 'CA'.

2.7 Apply DBSCAN

X_new_DBSCAN = X_new[:10000]

from sklearn.neighbors import NearestNeighbors
model = NearestNeighbors(n_neighbors=5,radius=3.0).fit(X_new_DBSCAN)

dist,ind = model.kneighbors(X_new_DBSCAN)

five_knn = []
for i in dist:
    five_knn.append(i[4])

five_index = []
for i in ind:
    five_index.append(i[4])

df = pd.DataFrame({"index":five_index,"Fifth_NN":five_knn})
df.sort_values(by = "Fifth_NN",ascending=True,inplace=True)

fig = go.Figure()
fig.add_trace(go.Scatter(x = list(np.arange(1,10001,1)),y = list(df['Fifth_NN'].values),mode ='markers',name = "5th Nearest Neighbors",showlegend = True ))
fig.add_trace(go.Scatter(x = [9700],y = [1.4],mode = 'lines+markers',name = 'Elbow Point'))
fig.update_layout(title = 'index vs 5 NN dist',
                  shapes=[
                # Line Vertical
            go.layout.Shape(
            type="line",
            x0=0,
            y0=1.4,
            x1=9700,
            y1=1.4,
            line=dict(
                color="red",
                width=1
            ))],
                  xaxis = go.layout.XAxis(title = go.layout.xaxis.Title(text = 'Index Of DataPoints')),
                 yaxis = go.layout.YAxis(title = go.layout.yaxis.Title(text = "Euclidean distance of 5NN")))
fig.show()

model_DBSCAN = DBSCAN(eps=1.4,min_samples=5,n_jobs=-1)
model_DBSCAN.fit(X_new_DBSCAN)

DBSCAN(algorithm='auto', eps=1.4, leaf_size=30, metric='euclidean',
       metric_params=None, min_samples=5, n_jobs=-1, p=None)

X_new_DBSCAN_0 = X_train[:10000].loc[list(np.where(model_DBSCAN.labels_==0)[0])]
X_new_DBSCAN_1 = X_train[:10000].loc[list(np.where(model_DBSCAN.labels_==1)[0])]
X_new_DBSCAN_2 = X_train[:10000].loc[list(np.where(model_DBSCAN.labels_==2)[0])]
X_new_DBSCAN_3 =X_train[:10000].loc[list(np.where(model_DBSCAN.labels_==3)[0])]
X_new_DBSCAN_noise = X_train[:10000].loc[list(np.where(model_DBSCAN.labels_==-1)[0])]

WordCloud Visual Representation Of Each Cluster Data

WordCloud_visual(X_new_DBSCAN_0,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_DBSCAN_0['teacher_prefix'].value_counts().index ),
                        y =list(X_new_DBSCAN_0['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_DBSCAN_0['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1aff8dc1b70>

X_new_DBSCAN_0['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1aff8f62208>

WordCloud_visual(X_new_DBSCAN_1,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_DBSCAN_1['teacher_prefix'].value_counts().index ),
                        y =list(X_new_DBSCAN_1['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_DBSCAN_1['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1aff9b1fcf8>

X_new_DBSCAN_1['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1aff9b9f978>

WordCloud_visual(X_new_DBSCAN_2,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_DBSCAN_2['teacher_prefix'].value_counts().index ),
                        y =list(X_new_DBSCAN_2['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_DBSCAN_2['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1affa783320>

X_new_DBSCAN_2['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1affaf47b00>

WordCloud_visual(X_new_DBSCAN_3,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_DBSCAN_3['teacher_prefix'].value_counts().index ),
                        y =list(X_new_DBSCAN_3['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_DBSCAN_3['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1affc73a438>

X_new_DBSCAN_3['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1affb3508d0>

WordCloud_visual(X_new_DBSCAN_noise,stopwords)

from plotly import express as px
fig = go.Figure([go.Bar(x = list(X_new_DBSCAN_noise['teacher_prefix'].value_counts().index ),
                        y =list(X_new_DBSCAN_noise['teacher_prefix'].value_counts()))])
fig.show()

plt.figure(figsize = (20,10))
X_new_DBSCAN_noise['school_state'].value_counts().plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1b01857cd68>

X_new_DBSCAN_noise['project_grade_category'].value_counts().plot(kind = 'bar')

<matplotlib.axes._subplots.AxesSubplot at 0x1affdd8e668>

Summary on the cluster form by DBSCAN Algorithm

From the starting we have observed that cluster has unique teacher prefix category so in these cluster form by DBSCAN we have seen same trait .

In cluster 1 , we have seen cluster data points having only one teacher_prefix category which is 'mr'.

In cluster 2 , we have seen cluster data points having only one teacher_prefix category which is 'mrs'.

In cluster 3 , we have seen cluster data points having only one teacher_prefix category which is 'ms'.

In cluster 4 , we have seen cluster data points having only one teacher_prefix category which is 'teacher'.

In cluster 5 (noisy Cluster) , we have seen cluster data points having all teacher_prefix category which is 'mr','mrs','teacher','ms'.

Conclusion

from prettytable import PrettyTable

#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable

x = PrettyTable()
x.field_names = ["Featurization","Model", "Cluster Form","Best Hyperparameter"]

x.add_row(["TFIDF","K-Means" ,2,2])
x.add_row(["TFIDF","Agglomerative", 5,5])
x.add_row(["TFIDF","DBSCAN", 4 , "eps = 1.4" " and " "minpts =5"])
print(x)

+---------------+---------------+--------------+-------------------------+
| Featurization |     Model     | Cluster Form |   Best Hyperparameter   |
+---------------+---------------+--------------+-------------------------+
|     TFIDF     |    K-Means    |      2       |            2            |
|     TFIDF     | Agglomerative |      5       |            5            |
|     TFIDF     |     DBSCAN    |      4       | eps = 1.4 and minpts =5 |
+---------------+---------------+--------------+-------------------------+